期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2016
卷号：10
期号：7
页码：269-280
DOI：10.14257/ijsia.2016.10.7.24
出版社：SERSC
摘要：This paper construct the predicted model based on support vector machine (SVM) for the Shanghai Composite Index, acquired the model parameters using genetic algorithm optimization was carried out, combined with k-fold cross method. Experiments based on the start date to February 2011 total 4948 trading day data, 10 fold cross circulation experiments of GA optimization; get the most accurate model parameter of SVM. At last, the regression model is used to predict, and the relative error of regression prediction is 0.11, and the accuracy of regression prediction is higher. In conclusion, this model can be used to predict the Shanghai Composite Index.
关键词：SVM; genetic algorithm; K fold cross experiment; regression prediction
